Improving Metacomprehension And Learning Through Graduated Concept Mod

Mental model development, deeper levels of information processing, and elaboration are critical to learning. More so, individuals\u27 metacomprehension accuracy is integral to making improvements to their knowledge base. In other words, without an accurate perception of their knowledge on a topic, learners may not know that knowledge gaps or misperceptions exist and, thus, would be less likely to correct them. Therefore, this study offered a dual-process approach that aimed at enhancing metacomprehension. One path aimed at advancing knowledge structure development and, thus, mental model development. The other focused on promoting a deeper level of information processing through processes like elaboration. It was predicted that this iterative approach would culminate in improved metacomprehension and increased learning. Accordingly, using the Graduated Concept Model Development (GCMD) approach, the role of learner-generated concept model development in facilitating metacomprehension and knowledge acquisition was examined. Concept maps have had many roles in the learning process as mental model assessment tools and advanced organizers. However, this study examined the process of concept model building as an effective training tool. Whereas, concept maps functioning as advanced organizers are certainly beneficial, it would seem that the benefits of having a learner examine and amend the current state of their knowledge through concept model development would prove more effective for learning. In other words, learners looking at an advanced organizer of the training material may feel assured that they have a thorough understanding of it. Only when they are forced to create a representation of the material would the gaps and misperceptions in their knowledge base likely be revealed. In short, advanced organizers seem to rely on recognition, where concept model development likely requires recalling and understanding \u27how\u27 and \u27why\u27 the interrelationships between concepts exist. Therefore, the Graduated Concept Model Development (GCMD) technique offered in this study was based on the theory that knowledge acquisition improves when learners integrate new information into existing knowledge, assign elaborated meanings to concepts, correct misperceptions, close knowledge gaps, and strengthen accurate connections between concepts by posing targeted questions against their existing knowledge structures. This study placed an emphasis on meaningful learning and suggested a process by which newly introduced concepts would be manipulated for the purpose of improving metacomprehension by strengthening accurate knowledge structures and mental model development, and through deeper and elaborated information processing. Indeed, central to improving knowledge deficiencies and misunderstandings is metacomprehension, and the constructing of concepts maps was hypothesized to improve metacomprehension accuracy and, thus, learning. This study was a one-factor between-groups design with concept map type as the independent variable, manipulated at four levels: no concept map, concept map as advanced organizer, learner-built concept map with feedback, and learner-built concept map without feedback. The dependent variables included performance (percent correct) on a declarative and integrative knowledge assessment, mental model development, and metacomprehension accuracy. Participants were 68 (34 female, 34 male, ages 18-35, mean age = 21.43) undergraduate students from a major southeastern university. Participants were randomly assigned to one of the four experimental conditions, and analysis revealed no significant differences between the groups. Upon arrival, participants were randomly assigned to one of the four experimental conditions. Participants then progressed through the three stages of the experiment. In Stage I, participants completed forms regarding informed consent, general biographical information, and task self-efficacy. In Stage II, participants completed the self-paced tutorial based on the Distributed Dynamic Decision Making (DDD) model, a simulated military command and control environment aimed at creating events to encourage team coordination and performance (for a detailed description, see Kleinman & Serfaty, 1989). The manner by which participants worked through the tutorial was determined by their assigned concept map condition. Upon finishing each module of the tutorial, participants then completed a metacomprehension prediction question. In Stage III, participants completed the computer-based knowledge assessment test, covering both declarative and integrative knowledge, followed by the metacomprehension postdiction question. Participants then completed the card sort task, as the assessment of mental model development. Finally, participants completed a general study survey and were debriefed as to the purpose of the study. The entire experiment lasted approximately 2 to 3 hours. Results indicated that the GCMD condition showed a stronger indication of metacomprehension accuracy, via prediction measures, compared with the other three conditions (control, advanced organizer, and feedback), and, specifically, significantly higher correlations than the other three conditions in declarative knowledge. Self-efficacy measures also indicated that the higher metacomprehension accuracy correlation observed in the GCMD condition was likely the result of the intervention, and not due to differences in self-efficacy in that group of participants. Likewise, the feedback and GCMD conditions led to significantly high correlations for metacomprehension accuracy based on levels of understanding on the declarative knowledge tutorial module (Module 1). The feedback condition also showed similar responses for the integrative knowledge module (Module 2). The advanced organizer, feedback, and GCMD conditions were also found to have significantly high correlation of self-reported postdiction of performance on the knowledge assessment and the actual results of the knowledge assessment results. However, results also indicated that there were no significant findings between the four conditions in mental model assessment and knowledge assessment. Nevertheless, results support the relevance of accurate mental model development in knowledge assessment outcomes. Retrospectively, two opposing factors may have complicated efforts to detect additional differences between groups. From one side, the experimental measures may not have been rigorous enough to filter out the effect from the intervention itself. Conversely, software usability issues and the resulting limitations in experimental design may have worked negatively against the two concept mapping conditions and, inadvertently, suppressed effects of the intervention. Future research in the GCMD approach will likely review cognitive workload, concept mapping software design, and the sensitivity of the measures involved

Identifying Factors Influencing Senior Leader Technology Readiness

What influences a person's attitude toward technology varies greatly. Does a person's attitude toward technology changes over time? What factors influence changes in attitude towards technology? This dissertation research provides an understanding of Technology Readiness (TR) over time and the factors influencing resultant conditions. The primary factors explored in this research include group interaction, the role of facilitators and training. This study used the quantitative research paradigm. The principle measure of the effects of the factors was Parasuraman and Colby's Technology Readiness Index (TRI). TR provided a mechanism to evaluate factors influencing Senior Leader Technology Readiness. Technology Readiness is predominantly about an individual’s willingness to adopt or embrace technology. TR is a set of technological beliefs and asserts ones technological competence (Parasuraman, 2000). Understanding individual TR and the propensity for technology adoption is important, particularly in organizations where technology is critical to success. Gartner predicts by 2017, half of employers will require employees to provide their own device for work. (Gartner 2013). Tangentially, mobile initiatives are putting pressure on the work force to use and understand technology. From a practitioner’s standpoint, how do companies know where current employees or future candidates stand regarding their technology competence and importantly the willingness to adopt? Parasuraman and Colby provided empirical evidence, through their quantitative and qualitative research, that individuals possess both positive and negative technology beliefs. This research examines whether cohort-style learning, electronic delivery of information and informal training influences a person's TR. The results of this study indicate two dimensions were consistent across the study and two dimensions (innovativeness and discomfort) varied between the initial and last data collection points. Both of these latter two dimensions displayed statistical significance between the two data collection points. Additionally, two of the dimensions (innovativeness and optimism) predicted an individual’s willingness to use their iPad by providing a statistically significant correlation between these two dimensions and device application downloads. Lastly, the treatment group receiving both treatments accounted for a statistically significant Technology Readiness change

Investigating users’ mental models of traditional and digital libraries

There is much HCI-related literature on mental models and on the usability of digital libraries, however there is no previously published literature on users’ mental models of either traditional or digital libraries. This is surprising, since many digital libraries are difficult to use and it is not immediately clear why. Our study begins to fill this void by examining users’ mental models of traditional and digital libraries through a series of Contextual Inquiry interviews that mix traditional think-aloud observations, which usually demand minimal researcher intervention, and semi-structured interviews, which usually demand significant intervention. The study finds that participants’ mental models of traditional and digital libraries extend beyond surface similarities and differences, such as the hierarchical organisation of items in both types of library and the availability of documents in paper and electronic mediums. These models contain deeper similarities and differences based on the information-seeking goals that can be fulfilled by each type of library, issues concerning the contents and relevance of individual documents and entire libraries, and ‘how searching works’ and how to ‘troubleshoot’ in both types of library. Although the use of concrete analogies to influence users’ understanding or usage of digital libraries was not widespread, participants used their knowledge of Internet search engines to infer how searching might work in digital libraries. Additionally, most participants assumed that even if different at the interface level or at the level of the underlying technology employed, the search components of digital libraries, Internet search engines and other digital entities work in a similar way to bring back search results. The study also finds that a large component of users’ mental models of digital libraries is the notion of access restrictions. The insights gained from the observations relating to the above recurring themes in users’ mental models are discussed with a view of helping to improve digital library usability by reducing access restrictions and notifying users of any such restrictions upfront, by providing dynamic and context-dependent help to users, by carefully introducing analogies into the digital library interface (if and where appropriate) and by making multiple digital libraries searchable under a single front-end to enable them to be accessed, browsed and searched in the same way

Investigating users’ mental models of traditional and digital libraries

There is much HCI-related literature on mental models and on the usability of digital libraries, however there is no previously published literature on users’ mental models of either traditional or digital libraries. This is surprising, since many digital libraries are difficult to use and it is not immediately clear why. Our study begins to fill this void by examining users’ mental models of traditional and digital libraries through a series of Contextual Inquiry interviews that mix traditional think-aloud observations, which usually demand minimal researcher intervention, and semi-structured interviews, which usually demand significant intervention. The study finds that participants’ mental models of traditional and digital libraries extend beyond surface similarities and differences, such as the hierarchical organisation of items in both types of library and the availability of documents in paper and electronic mediums. These models contain deeper similarities and differences based on the information-seeking goals that can be fulfilled by each type of library, issues concerning the contents and relevance of individual documents and entire libraries, and ‘how searching works’ and how to ‘troubleshoot’ in both types of library. Although the use of concrete analogies to influence users’ understanding or usage of digital libraries was not widespread, participants used their knowledge of Internet search engines to infer how searching might work in digital libraries. Additionally, most participants assumed that even if different at the interface level or at the level of the underlying technology employed, the search components of digital libraries, Internet search engines and other digital entities work in a similar way to bring back search results. The study also finds that a large component of users’ mental models of digital libraries is the notion of access restrictions. The insights gained from the observations relating to the above recurring themes in users’ mental models are discussed with a view of helping to improve digital library usability by reducing access restrictions and notifying users of any such restrictions upfront, by providing dynamic and context-dependent help to users, by carefully introducing analogies into the digital library interface (if and where appropriate) and by making multiple digital libraries searchable under a single front-end to enable them to be accessed, browsed and searched in the same way

The construction of mental models of information-rich web spaces: the development process and the impact of task complexity

This study investigated the dynamic process of people constructing mental models of an information-rich web space during their interactions with the system and the impact of task complexity on model construction. In the study, subjects' mental models of MedlinePlus were measured at three time points: after subjects freely explored the system for 5 minutes, after the first search session, and after the second search session. During the first search session, the 39 subjects were randomly divided into two groups; one group completed 12 simple search tasks and the other group completed 3 complex search tasks. During the second search session, all subjects completed a set of 4 simple tasks and 2 complex tasks. Measures of the subjects' mental models included a concept listing protocol, a semi-structured interview, and a drawing task. The analysis revealed that subjects' mental models were a rich representation of the cognitive and emotional processes involved in their interaction with information systems. The mental models consisted of three dimensions (structure, evaluation and emotion, and (expected) behaviors); the structure and evaluation/emotion dimensions consisted of four components each: system, content, information organization, and interface. The construction of mental models was a process coordinated by people's internal cognitive structure and the external sources (the system, system feedback, and tasks) and a process distributed through time, in the sense that earlier mental models impacted later ones. Task complexity also impacted the construction of mental models by influencing what objects in the system were perceived and represented by the user, the specificity of the representations, and the user's feelings about the objects. Based on the study results, recommendations for employing mental models as a tool to assist designers in constructing user models, eliciting user requirements, and performing usability evaluations are put forward